DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

(2023) Christian Moya et al.   NEUROCOMPUTING

A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

(2022) Somdatta Goswami et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A data-centric weak supervised learning for highway traffic incident detection

(2022) Yixuan Sun et al.   ACCIDENT ANALYSIS AND PREVENTION

MIONet: Learning Multiple-Input Operators via Tensor Product

(2022) Pengzhan Jin et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks

(2021) Shengze Cai et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A Comprehensive Survey on Graph Neural Networks

(2020) Zonghan Wu et al.   IEEE Transactions on Neural Networks and Learning Systems

ConvPDE-UQ: Convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains

(2019) Nick Winovich et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data driven governing equations approximation using deep neural networks

(2019) Tong Qin et al.   JOURNAL OF COMPUTATIONAL PHYSICS

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

(2019) Ling Zhao et al.   IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

Robust data-driven discovery of governing physical laws with error bars

(2018) Sheng Zhang et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

(2018) E. Kaiser et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Learning partial differential equations via data discovery and sparse optimization

(2017) Hayden Schaeffer   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Big data and its technical challenges

(2014) H. V. Jagadish et al.   COMMUNICATIONS OF THE ACM

Wavelets on graphs via spectral graph theory

(2010) David K. Hammond et al.   APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS